Optimal selection of time windows for preventive maintenance of offshore wind farms subject to wake losses

Zhang, Junqiang; Chowdhury, Souma; Zhang, Jie; Tong, Weiyang; Messac, Achille

doi:10.1002/we.2815

Cited by 4 publications

(2 citation statements)

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“…Constraint (1) specifies the maintenance crew capacity limit, which ensures that the PMs started concurrently cannot be larger than m. Constraints ( 2)-( 4) specify that once a PM is started at period t, then the PM will be conducted for a time length of h. Constraint (5) ensures that the PM cannot be conducted in those periods with bad weather conditions. Constraint (6) requires that any one PM for any wind turbine must be supplied by a maintenance vessel. Constraint (7) means that any conducted PM must be finished before the end period |T|.…”

Section: Mathematical Modelingmentioning

confidence: 99%

“…Such studies contribute to the development of robust maintenance strategies and underscore the essential nature of maintenance in optimizing offshore wind farm performance [5]. To uphold system reliability above a crucial threshold and minimize unexpected failures, the implementation of routine preventive maintenances (PMs) is imperative [6]. These PMs serve to inspect, identify, and rectify potential component failures.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

See 1 more Smart Citation

Sustainable Operation and Maintenance of Offshore Wind Farms Based on the Deep Wind Forecasting

Zhou,

Ke,

Zhu

et al. 2023

Sustainability

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Offshore wind farms are becoming a pivotal solution to address the increasing energy demand worldwide and reduce carbon emissions to achieve a sustainable energy sector. Considering the higher operational and maintenance cost of offshore wind farms, it is important to make a good maintenance plan to guarantee the system’s reliability and reduce the total cost related to maintenance activities at the same time. Because maintenance planning is a long-term decision problem and the wind force is random, long-term wind force prediction is needed to help managers evaluate the loss caused by maintenances to be executed in the future. However, long-term wind force prediction is naturally complicated, which is much harder than the short-term (e.g., day-ahead) prediction widely investigated in the literature. In order to overcome this difficulty, we design a deep learning framework combining variational mode decomposition, a convolution neural network, long short-term memory network, and full-connected network. Using the public data from the city of Leeds, the prediction accuracy of the above framework is validated by comparing it with other prediction techniques. Then, the predicted wind force is input into the established optimization model determining preventive maintenances during a predefined period. Because the uncertainty of wind force is replaced by the prediction value, the optimization model can be established as a mixed-integer linear programing model, which only contains limited variables and can be solved quickly. Lastly, an abundance of numerical experiments are conducted to validate the effectiveness of the proposed optimization model, based on which some managerial insights are provided to the managers of offshore wind farms about the optimal operations and maintenance strategy. The research outcome will greatly promote the development of the wind power industry in the future.

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Section: Mathematical Modelingmentioning

confidence: 99%

Section: Introduction 1backgroundmentioning

confidence: 99%